{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trajectory Tracking with Integral Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchdyn.core import NeuralODE\n",
    "from torchdyn.nn import *\n",
    "from torchdyn.datasets import *\n",
    "from torchdyn.utils import *\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# quick run for automated notebook validation\n",
    "dry_run = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integral Loss\n",
    "\n",
    "We consider a loss of type\n",
    "\n",
    "$$\n",
    "    \\ell_\\theta := \\int_0^S g(z(t))d\\tau\n",
    "$$\n",
    "\n",
    "where $z(s)$ satisfies the neural ODE initial value problem \n",
    "\n",
    "$$\n",
    "    \\left\\{\n",
    "    \\begin{aligned}\n",
    "        \\dot{z}(t) &= f(z(t), \\theta(t))\\\\\n",
    "        z(0) &= x\n",
    "    \\end{aligned}\n",
    "    \\right. \\quad t\\in[0,T]\n",
    "$$\n",
    "\n",
    "where $\\theta(t)$ is parametrized with some spectral method (`Galerkin-style`),  i.e. $\\theta(t)=\\theta(t,\\omega)$ [$\\omega$: parameters of $\\theta(t)$].\n",
    "\n",
    "**REMARK:** In `torchdyn`, we do not need to evaluate the following integral in the forward pass of the ODE integration.\n",
    "In fact, we will compute the gradient $d\\ell/d\\omega$ just by solving backward \n",
    "\n",
    "$$\n",
    "    \\begin{aligned}\n",
    "        \\dot\\lambda (t) &= -\\frac{\\partial f}{\\partial z}\\lambda(t) + \\frac{\\partial g}{\\partial z} \\\\\n",
    "        \\dot\\mu (t) &= -\\frac{\\partial f}{\\partial \\theta}\\frac{\\partial \\theta}{\\partial \\omega}\\lambda(t)\n",
    "    \\end{aligned}~~\\text{with}~~\n",
    "    \\begin{aligned}\n",
    "        \\lambda (T) &= 0\\\\\n",
    "        \\mu (T) &= 0\n",
    "    \\end{aligned}\n",
    "$$\n",
    "\n",
    "and, $\\frac{d\\ell}{d\\omega} = \\mu(0)$. Check out [this paper](https://arxiv.org/abs/2003.08063) for more details on the integral adjoint for Neural ODEs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example:** Use a neural ODE to track a 2D curve $\\gamma:[0,\\infty]\\rightarrow \\mathbb{S}_2$ ($\\mathbb{S}_2$: unit circle, $\\mathbb{S}_2:=\\{x\\in\\mathbb{R}^2:||x||_2=1\\}$), i.e.\n",
    "\n",
    "$$\n",
    "    \\gamma(s) := [\\cos(2\\pi s), \\sin(2\\pi s)]\n",
    "$$\n",
    "\n",
    "which has periodicity equal to $1$: $\\forall n\\in\\mathbb{N}, \\forall s\\in[0,\\infty]~~\\gamma(s) = \\gamma(ns)$.\n",
    "\n",
    "Let suppose to train the neural ODE for $s\\in[0,1]$. Therefore we can easily setup the integral cost as\n",
    "\n",
    "$$\n",
    "    \\ell_\\theta := \\int_0^1 (h(\\tau)-\\gamma(\\tau))^2 d\\tau\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simple Callables are also fine as integral losses, as long as they're callable with `t, x`. We use nn.Modules for convenience, since\n",
    "# we compute the reference signal here as well\n",
    "\n",
    "class SineReferenceIntegralLoss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "    def y(self, t):\n",
    "        return torch.tensor([torch.cos(2*np.pi*t), torch.sin(2*np.pi*t)])[None, :].to(t)\n",
    "        \n",
    "    def forward(self, t, x):\n",
    "        int_loss = ((self.y(t)-x)**2).sum(1, keepdim=True)\n",
    "        return int_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** In this case we do not define any dataset of initial conditions and load it into the dataloader. Instead, at each step, we will sample new ICs from a normal distrubution centered in $\\gamma(0) = [1,0]$\n",
    "\n",
    "$$\n",
    "    x_t \\sim \\mathcal{N}(\\gamma(0),0.1)\n",
    "$$\n",
    "\n",
    "However, we still define a \"dummy\" `trainloader` to use `pythorch_lightning`'s API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummy trainloader\n",
    "train = torch.utils.data.TensorDataset(torch.zeros(1), torch.zeros(1))\n",
    "trainloader = torch.utils.data.DataLoader(train, batch_size=1, shuffle=True)\n",
    "t_span = torch.linspace(0, 2, 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Learner**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pytorch_lightning as pl\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "class Learner(pl.LightningModule):\n",
    "    def __init__(self, model:nn.Module):\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "        self.iterations = 0\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.model(x)    \n",
    "    \n",
    "    def training_step(self, batch, batch_idx):    \n",
    "        self.iterations += 1\n",
    "        # We sample from Normal distribution around \"nominal\" initial condition\n",
    "        x = torch.tensor([1.,0.])\n",
    "        x = (x + 0.5*torch.randn(4096, 2)).to(device)\n",
    "        _, y_hat = self.model(x, t_span)\n",
    "        y_hat = y_hat[-1]\n",
    "        \n",
    "        # We need to evaluate a \"dummy loss\" (just the summed output of the model)\n",
    "        # to construct the graph for the 'backward()' \"triggering\" integral adjoint computation \n",
    "        loss = 0.*y_hat.sum()\n",
    "        intloss = None\n",
    "        \n",
    "        if self.iterations % 10 == 0:\n",
    "            with torch.no_grad():\n",
    "            # compute integral loss explicitly by augmentation of state with \n",
    "            # an aux. variable. See `Dissecting Neural ODEs` for more information\n",
    "                model.sensitivity = model.vf.sensitivity = 'autograd'  \n",
    "                x = torch.cat([torch.zeros(x.shape[0], 1).to(x), x], 1)\n",
    "                _, sol = self.model(x, t_span)\n",
    "                intloss = sol[-1, :, :1].mean()\n",
    "                model.sensitivity = model.vf.sensitivity = 'interpolated_adjoint'\n",
    "            self.log('intloss', intloss, prog_bar=True)\n",
    "\n",
    "        return {'loss': loss}   \n",
    "    \n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.model.parameters(), lr=3e-3)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return trainloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter Varying Neural ODE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your vector field callable (nn.Module) should have both time `t` and state `x` as arguments, we've wrapped it for you.\n"
     ]
    }
   ],
   "source": [
    "# We use a Galerkin Neural ODE with one hidden layer, \n",
    "# Fourier spectrum (period=1) and only 2 freq.s\n",
    "f = nn.Sequential(nn.Linear(2, 64),\n",
    "                  nn.Tanh(),\n",
    "                  nn.Linear(64, 2)).to(device)\n",
    "\n",
    "# Define the model\n",
    "model = NeuralODE(f, \n",
    "                 solver='rk4',\n",
    "                 sensitivity='interpolated_adjoint',\n",
    "                 solver_adjoint='dopri5',\n",
    "                 atol_adjoint=1e-5,\n",
    "                 rtol_adjoint=1e-5,\n",
    "                 integral_loss=SineReferenceIntegralLoss()).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name  | Type      | Params\n",
      "------------------------------------\n",
      "0 | model | NeuralODE | 322   \n",
      "------------------------------------\n",
      "322       Trainable params\n",
      "0         Non-trainable params\n",
      "322       Total params\n",
      "0.001     Total estimated model params size (MB)\n",
      "/home/michael/.cache/pypoetry/virtualenvs/torchdyn-voYSR01p-py3.8/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  warnings.warn(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78ba6da12f7f4df2a216f69a9a2a1aad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train the model (will take a while)\n",
    "learn = Learner(model)\n",
    "if dry_run: trainer = pl.Trainer(max_epochs=1, gpus=0)\n",
    "else: trainer = pl.Trainer(max_epochs=1000, gpus=1)\n",
    "trainer.fit(learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plots**\n",
    "\n",
    "We first evaluate the model on a test set of initial conditions sampled from $\\mathcal{N}(\\gamma(0),\\sigma\\mathbb{I})$. Since we trained the neural ODE with $\\sigma=0.1$, now we test it with $\\sigma=0.2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_span = torch.linspace(0, 12, 460).to(device)\n",
    "x_test = torch.tensor([1.,0.])\n",
    "x_test = (x_test + 0.2*torch.randn(100, 2)).to(device)\n",
    "model = model.to(device)\n",
    "trajectory = model.trajectory(x_test, t_span)\n",
    "trajectory = trajectory.detach().cpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Depth evolution of the system** -> The system is trained for $s\\in[0,1]$ and then we extrapolate until $s=5$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "K = 60\n",
    "t_span = t_span.cpu()\n",
    "y1 = np.cos(2*np.pi*t_span)\n",
    "y2 = np.sin(2*np.pi*t_span)\n",
    "\n",
    "fig = plt.figure(figsize=(8,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(t_span[:K], y1[:K], color='orange',alpha=0.5)\n",
    "ax.scatter(t_span[:K], y2[:K], color='orange',alpha=0.5)\n",
    "ax.scatter(t_span[K:], y1[K:], color='red',alpha=0.5)\n",
    "ax.scatter(t_span[K:], y2[K:], color='red',alpha=0.5)\n",
    "for i in range(len(x_test)):\n",
    "    ax.plot(t_span, trajectory[:,i,:], color='blue', alpha=.1)\n",
    "ax.set_xlabel(r\"$t$ [depth]\")\n",
    "ax.set_ylabel(r\"$z(t)$ [state]\")\n",
    "ax.set_title(r\"Depth evolution of the system\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**State-space trajectories**\n",
    "-> All the (random) IC converge to the desired trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(111)\n",
    "for i in range(len(x_test)):\n",
    "    ax.plot(trajectory[:,i,0], trajectory[:,i,1], color='blue', alpha=.1)\n",
    "    ax.scatter(trajectory[0,i,0], trajectory[0,i,1], color='black', alpha=.1)\n",
    "ax.plot(y1[:100],y2[:100], color='red')\n",
    "ax.set_xlabel(r\"$z_0$\")\n",
    "ax.set_ylabel(r\"$z_1$\")\n",
    "ax.set_title(r\"State-Space Trajectories\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torchdyn",
   "language": "python",
   "name": "torchdyn"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}